The present invention relates to the processing of signals obtained from a medical diagnostic apparatus, such as a pulse oximeter, using a digital filter to reduce noise effects.
A typical pulse oximeter measures two physiological parameters, percent oxygen saturation of arterial blood hemoglobin (SpO2 or sat) and pulse rate. Oxygen saturation can be estimated using various techniques. In one common technique, the photocurrent generated by the photo-detector is conditioned and processed to determine the ratio of modulation ratios (ratio of ratios) of the red to infrared signals. This modulation ratio has been observed to correlate well to arterial oxygen saturation. The pulse oximeters and sensors are empirically calibrated by measuring the modulation ratio over a range of in vivo measured arterial oxygen saturations (SaO2) on a set of patients, healthy volunteers, or animals. The observed correlation is used in an inverse manner to estimate blood oxygen saturation (SpO2) based on the measured value of modulation ratios of a patient. The estimation of oxygen saturation using modulation ratios is described in U.S. Pat. No. 5,853,364, entitled “METHOD AND APPARATUS FOR ESTIMATING PHYSIOLOGICAL PARAMETERS USING MODEL-BASED ADAPTIVE FILTERING,” issued Dec. 29, 1998, and U.S. Pat. No. 4,911,167, entitled “METHOD AND APPARATUS FOR DETECTING OPTICAL PULSES,” issued Mar. 27, 1990. The relationship between oxygen saturation and modulation ratio is further described in U.S. Pat. No. 5,645,059, entitled “MEDICAL SENSOR WITH MODULATED ENCODING SCHEME,” issued Jul. 8, 1997. Most pulse oximeters extract the plethysmographic signal having first determined saturation or pulse rate, both of which are susceptible to interference.
A challenge in pulse oximetry is in analyzing the data to obtain a reliable measure of a physiologic parameter in the presence of large interference sources. Various solutions to this challenge have included methods that assess the quality of the measured parameter and decide on displaying the measured value when it is deemed reliable based upon a signal quality. Another approach involves a heuristic-based signal extraction technology, where the obtained signals are processed based on a series of guesses of the ratio, and which require the algorithm to start with a guess of the ratio, which is an unknown. Both the signal-quality determining and the heuristic signal extraction technologies are attempts at separating out a reliable signal from an unreliable one, one method being a phenomenological one and the other being a heuristic one.
A known approach for the reduction of noise in medical diagnostic devices including pulse oximeters involves the use of an adaptive filter, such as an adaptive digital filter. The adaptive filter is actually a data processing algorithm, and in most typical applications, the filter is a computer program that is executed by a central processor. As such, the filter inherently incorporates discrete-time measurement samples rather than continuous time inputs. A type of digital filter that is used in pulse oximeter systems is a Kalman filter. While conventional adaptive digital filters in general and Kalman filters in particular have been assimilated in medical diagnostics system to help reduce noise in a signal, there are still many challenges that need to be addressed to improve the techniques that are used to reduce noise effects in signals; noise effects such as those present in a medical diagnostic device. One of the shortcomings of using a Kalman filter is that a Kalman filter is an adaptive filter whose functioning is mathematically-based and where its aim is to compare the output of the filter with a desired output, and reduce the error in the comparison by continuously varying the filter's coefficients. So, a Kalman filter generates filter coefficients in an adaptive manner to minimize an error. While this method has been adopted by many, it is still a method that is somewhat blind regarding the signal that it is being filtered. Such an approach does not take into account the unique attributes that an input signal may possess and which are physiologically based. Another shortcoming of the Kalman filtering is that the Kalman filter is linear in its input-output relationship. One can appreciate that in certain conditions, the requirement that the filter be linear in its input-output relationship is too constraining. Yet another shortcoming of a Kalman filter is that filter parameters are continuously tuned, which can be computationally expensive.
There is therefore a need to develop a filter for reducing noise effects in signals that does not suffer form the above-mentioned constraints of conventional adaptive filters.